Download PDFOpen PDF in browserBotnet Detection Using Recurrent Variational AutoencoderEasyChair Preprint 30299 pages•Date: March 22, 2020AbstractBotnets are increasingly used by malicious actors, creating increasing threat to a large number of internet users. We validate robustness of our method with the CTU-13 dataset, where we have chosen the testing dataset to have different types of botnets than those of training dataset. Tests show that RVAE is able to detect botnets with the same accuracy as the best known results published in literature. In addition, we propose an approach to assign anomaly score based on probability distributions, which allows us to detect botnets in steaming mode as the new networking statistics becomes available. This on-line detection capability would enable real-time detection of unknown botnets. Keyphrases: Anomaly Detection System, Botnet Detection, Network Security, Recurrent Neural Network, online detection, variational autoencoder
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